The recent rapid advances in machine learning algorithms, hardware, and platforms has had a major effect on fielded applications—access to data has become the limiting constraint in many such applications. The limitation often arises because data is generated in a decentralized fashion, held privately under a variety of ownership models. There is accordingly a growing need to study distributed machine learning problems in which self-interested agents are incentivized to share their data. Designing such incentives requires understanding the costs that users incur in sharing their data, including communication and privacy costs, and trading off those costs against possible benefits from data analysis. The costs and benefits will generally be agent- and type-specific. The overall problem is a complex market-design problem that aims at incentivizing the sharing of data while achieving computational, economic, and statistical efficiencies [1]}, [2]}, [3]}.
